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Volumn 3129, Issue , 2004, Pages 622-627

An empirical study of building compact ensembles

Author keywords

Compact Ensemble; Ensemble Methods; Image Mining

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

EID: 35048864442     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-27772-9_63     Document Type: Article
Times cited : (15)

References (7)
  • 4
    • 35048825500 scopus 로고    scopus 로고
    • A unified decomposition of ensemble loss for predicting ensemble performance
    • M. Goebel, P. Riddle, and M. Barley. A unified decomposition of ensemble loss for predicting ensemble performance. In Proceedings of the 19th ICML, pages 211-218. 2002.
    • (2002) Proceedings of the 19th ICML , pp. 211-218
    • Goebel, M.1    Riddle, P.2    Barley, M.3
  • 5
    • 0007696417 scopus 로고    scopus 로고
    • Less is more: Active learning with support vector machines
    • G. Schohn and D. Cohn. Less is more: Active learning with support vector machines. In Proceedings of the 17th ICML, pages 839-846, 2000.
    • (2000) Proceedings of the 17th ICML , pp. 839-846
    • Schohn, G.1    Cohn, D.2
  • 7
    • 0038030864 scopus 로고    scopus 로고
    • Extracting symbolic rules from trained neural network ensembles
    • Z.-H. Zhou, Y. Jiang, and S.-F. Chen. Extracting symbolic rules from trained neural network ensembles. AI Commun., 16(1):3-15, 2003.
    • (2003) AI Commun. , vol.16 , Issue.1 , pp. 3-15
    • Zhou, Z.-H.1    Jiang, Y.2    Chen, S.-F.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.